\Header{partition.object}{Partitioning Object}
\alias{partition}{partition.object}
\keyword{cluster}{partition.object}
\begin{Description}\relax
The objects of class \code{"partition"} represent a partitioning of a
dataset into clusters.\end{Description}
\begin{Value}
a \code{"partition"} object is a list with the following
(and typically more) components:
\begin{ldescription}
\item[\code{clustering}] the clustering vector.  An integer vector of length \eqn{n}{}, the number of
observations, giving for each observation the number (`id') of the
cluster to which it belongs.
\item[\code{call}] the matched \code{\Link{call}} generating the object.
\item[\code{silinfo}] a list with all \emph{silhouette} information, only available when
the number of clusters is non-trivial, i.e., \eqn{1 < k < n}{} and
then has the following components, see \code{\Link{silhouette}}
\describe{
\item[widths] an (n x 3) matrix, as returned by
\code{\Link{silhouette}()}, with for each observation i the
cluster to which i belongs, as well as the neighbor cluster of i
(the cluster, not containing i, for which the average
dissimilarity between its observations and i is minimal), and
the silhouette width \eqn{s(i)}{} of the observation.

\item[clus.avg.widths] the average silhouette width per cluster.
\item[avg.width] the average silhouette width for the dataset, i.e.,
simply the average of \eqn{s(i)}{} over all observations \eqn{i}{}.
}
This information is also needed to construct a \emph{silhouette plot} of
the clustering, see \code{\Link{plot.partition}}.

Note that \code{avg.width} can be maximized over different
clusterings (e.g. with varying number of clusters) to choose an
\emph{optimal} clustering.

\item[\code{objective}] value of criterion maximized during the
partitioning algorithm, may more than one entry for different stages.
\item[\code{diss}] an object of class \code{"dissimilarity"}, representing the total
dissimilarity matrix of the dataset (or relevant subset, e.g. for
\code{clara}).

\item[\code{data}] a matrix containing the original or standardized data.  This might
be missing to save memory or when a dissimilarity matrix was given
as input structure to the clustering method.

\end{ldescription}
\end{Value}
\begin{Section}{GENERATION}
These objects are returned from \code{pam}, \code{clara} or \code{fanny}.\end{Section}
\begin{Section}{METHODS}
The \code{"partition"} class has a method for the following generic functions:
\code{plot}, \code{clusplot}.\end{Section}
\begin{Section}{INHERITANCE}
The following classes inherit from class \code{"partition"} :
\code{"pam"}, \code{"clara"} and \code{"fanny"}.

See \code{\Link{pam.object}}, \code{\Link{clara.object}} and
\code{\Link{fanny.object}} for details.\end{Section}
\begin{SeeAlso}\relax
\code{\Link{pam}}, \code{\Link{clara}}, \code{\Link{fanny}}.\end{SeeAlso}

